Ethics & Research

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Transcript Ethics & Research

A short explanation of various
statistical concepts
Communication Research
Week 9
Making the Case
for Quantitative Research
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Advantages
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Tradition and history implies rigour
Numbers and statistics allows precise and exact
comparisons
Generalisation of findings
Limitations
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Cannot capture complexity of communication
over time
Difficult to apply outside of controlled
environments
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Basics of descriptive statistics
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Statisticians use mathematical methods to
analyse, summarise and interpret data that
have been collected
The choice of statistical method of analysis
depends on the data that have to be
analysed (and the experience of the
researcher)
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Descriptive vs inferential statistics
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Descriptive statistics refer to methods used
to obtain, from raw data, information that
characterises or summarises just that set of
data
Inferential statistics allow us to generalise
from the data collected to the general
population they were taken from
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Different statistical measures
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Raw data is unorganised but can be
tabulated to make it easier to understand
and to interpret
It is usually presented as a frequency table
or graph
A frequency chart will allow a researcher to
see trends or groupings of data and how
they are distributed
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Characteristics of each distribution
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Location – where on the axis is the distribution
positioned?
Dispersion – how broad is the distribution?
Shape – what is the form (appearance, pattern) of
the distribution?
The type of data you have to analyse will determine
the statistical measure chosen
Statistics describing the location of the distribution
are called measures of central tendency
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Measures of central tendency – the
mean
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The mean is the sum of all observed data values
divided by the sample size (the arithmetic average)
Describing data that are interval or ratio in nature
(eg speed of response, age in years) calls for the
mean
One of the main disadvantages is that it is most
profoundly affected by extreme scores
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Calculating a Mean Score
Scores:
79
81
82
86
86
88
91
93
95
97
total = 878
Divide by n = 10 scores
Mean = 87.8
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Measures of central tendency – the
median
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The median is the score or the point of distribution above which
one half of the scores lie eg in a simple set of scores such as 1, 3,
& 5 the median is 3
The median is best suited to data that are ordinal or ranked ( eg
birth order, rank in class)
To compute the median
 Order the scores from lowest to highest
 Count the number of scores
 Select the middle score
When the number of scores is even, find the mean of the two
middle scores
 eg 31 33 35 38 40 41 42 43 44 46 47 48 49 50
 N = 14 (no of scores); Median = (42 + 43) ÷ 2 = 42.5
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Two distributions of scores
Distribution 1
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Distribution 2
24
24
25
25
26
26
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Mean = 25
Range = 3
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19
22
25
28
30
35
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Mean = 25
Range = 20
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Measures of central tendency – the
mode
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The mode is the most frequently observed value in the
frequency distribution ie it is the score that occurs most
frequently
The mode is best used for nominal data and for data that are
qualitative in nature such as gender, eye colour, ethnicity,
school or group membership
In the following list of numbers:
58 27 24 41 27 26 41 53 24 29 41 53 47 28 56
 The mode is 41 because it occurs 3 times
A common mistake is to identify the mode as how frequently the
value occurs (3) not the value itself (41)
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Which measure when?
Which measure of central tendency?
Measure
Level of measurement
Examples
Mode
Nominal or categorical – ie
qualitative
Median
Ordinal or ranked
Rank in class, birth order
Mean
Interval and ratio
Speed of response, age in years
Gender, hair or eye colour, group
membership, ethnicity, school etc
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Three Measures of Variability
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Range: the difference between the highest and
lowest scores in a distribution of scores.
Variance: a measure of dispersion indicating the
degree to which scores cluster around the mean
score.
Standard deviation: index of the amount of variation
in a distribution of scores.
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Standard deviation
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SD is a measure of the variability indicating the
degree to which all observed values deviate from
the mean
SD can only be used for interval and ratio data, not
nominal data (eg gender)
It is the most frequently used statistic as a
measure of dispersion or variability
The larger the SD, the more variable the set of
scores is
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COMPUTING DEVIATION SCORES
Raw
Mean DEV. SQUARED
score
score
deviation score
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- 10
= -6
36
8
- 10
= -2
4
9
- 10
= -1
1
10
- 10
= 0
0
10
- 10
= 0
0
10
- 10
= 0
0
12
- 10
= 2
4
13
- 10
= 3
9
14
- 10
= 4
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90/9 = 10.00 = MEAN
70/9 = 7.77 = Variance
STANDARD DEVIATION: (Square Root of Variance) = 2.79
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Types of Variables
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Variable
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Element that is identified in the hypothesis or
research question
Property or characteristic of people or things
that varies in quality or magnitude
Must have two or more levels
Must be identified as independent or
dependent
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Independent Variables
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Manipulation or variation of this variable is
the cause of change in other variables
(the cause)
Technically, independent variable is the
term reserved for experimental studies
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Also called antecedent variable, experimental
variable, treatment variable, causal variable,
predictor variable
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Dependent Variables
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The variable of primary interest that is
measured (the effect)
Research question/hypothesis describes,
explains, or predicts changes in it
The variable that is influenced or changed by
the independent variable
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In non-experimental research, also called criterion
variable, outcome variable
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Relationship Between Independent and
Dependent Variables
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Cannot specify independent variables
without specifying dependent variables
Number of independent and dependent
variables depends on the nature and
complexity of the study
The number and type of variables dictates
which statistical test will be used
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Issues of Reliability and Validity
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Reliability = consistency in procedures and in
reactions of participants
Validity = truth - Does it measure what it
intended to measure?
When reliability and validity are achieved,
data are free from systematic errors
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Threats to Reliability and Validity
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If measuring device cannot make fine
distinctions
If measuring device cannot capture
people/things that differ
When attempting to measure something
irrelevant or unknown to respondent
Can measuring device really capture the
phenomenon?
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Other Sources of Variation
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Variation must represent true differences
Other sources of variation
 Factors not measured
 Personal factors
 Differences in situational factors
 Differences in research administration
 Number of items measured
 Unclear measuring device
 Mechanical or procedural issues
 Statistical processing of data
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